# Libraries
import pandas as pd
import numpy as np
import requests, io, os
import matplotlib.pyplot as plt
import folium

pd.set_option("display.max_colwidth", 200)

def fetch_dataset_csv(base_url, dataset, api_key=None):
    suffix = 'exports/csv?delimiter=%3B&list_separator=%2C&quote_all=false&with_bom=true'
    url = f"{base_url}{dataset}/{suffix}"
    params = {'api_key': api_key} if api_key else {}
    r = requests.get(url, params=params, timeout=30)
    r.raise_for_status()
    return pd.read_csv(io.BytesIO(r.content), delimiter=';')

API_KEY = os.environ.get('MELBOURNE_API_KEY', '')
BASE_URL = 'https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/'

DATASETS = {
    "business": "business-establishments-with-address-and-industry-classification",
    "cafes": "cafes-and-restaurants-with-seating-capacity",
    "pedestrian": "pedestrian-counting-system-monthly-counts-per-hour",
    "locations": "pedestrian-counting-system-sensor-locations"
}


business_df  = fetch_dataset_csv(BASE_URL, DATASETS["business"], API_KEY)
cafes_df     = fetch_dataset_csv(BASE_URL, DATASETS["cafes"], API_KEY)
ped_df       = fetch_dataset_csv(BASE_URL, DATASETS["pedestrian"], API_KEY)
loc_df       = fetch_dataset_csv(BASE_URL, DATASETS["locations"], API_KEY)

print("Business columns:", business_df.columns.tolist()[:12], "... | rows:", len(business_df))
print("Cafes columns   :", cafes_df.columns.tolist()[:12],    "... | rows:", len(cafes_df))
print("Ped columns     :", ped_df.columns.tolist()[:12],      "... | rows:", len(ped_df))
print("Loc columns     :", loc_df.columns.tolist()[:12],      "... | rows:", len(loc_df))

Business columns: ['census_year', 'block_id', 'property_id', 'base_property_id', 'clue_small_area', 'trading_name', 'business_address', 'industry_anzsic4_code', 'industry_anzsic4_description', 'longitude', 'latitude', 'point'] ... | rows: 393878
Cafes columns   : ['census_year', 'block_id', 'property_id', 'base_property_id', 'building_address', 'clue_small_area', 'trading_name', 'business_address', 'industry_anzsic4_code', 'industry_anzsic4_description', 'seating_type', 'number_of_seats'] ... | rows: 63121
Ped columns     : ['id', 'location_id', 'sensing_date', 'hourday', 'direction_1', 'direction_2', 'pedestriancount', 'sensor_name', 'location'] ... | rows: 1405267
Loc columns     : ['location_id', 'sensor_description', 'sensor_name', 'installation_date', 'note', 'location_type', 'status', 'direction_1', 'direction_2', 'latitude', 'longitude', 'location'] ... | rows: 143

business_df = business_df.copy()
cafes_df    = cafes_df.copy()
ped_df      = ped_df.copy()
loc_df      = loc_df.copy()

# Standardise coordinates to numeric
for df in (business_df, cafes_df, loc_df):
    if 'latitude' in df.columns:
        df['latitude']  = pd.to_numeric(df['latitude'], errors='coerce')
    if 'longitude' in df.columns:
        df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce')

# Cafes: normalise name column if present
name_cols = [c for c in cafes_df.columns if c.lower() in ['trading_name','business_name','name','organisation']]
if name_cols:
    cafes_df.rename(columns={name_cols[0]: 'business_name'}, inplace=True)
else:
    cafes_df['business_name'] = "Unnamed Café/Restaurant"

# Drop rows without coordinates
business_df = business_df.dropna(subset=['latitude','longitude']).copy()
cafes_df    = cafes_df.dropna(subset=['latitude','longitude']).copy()
loc_df      = loc_df.dropna(subset=['latitude','longitude']).copy()

business_df.head(3), cafes_df[['business_name','latitude','longitude']].head(3), loc_df.head(3)

(   census_year  block_id  property_id  base_property_id clue_small_area  \
 0         2010      1101       110843            110843       Docklands   
 1         2010      1101       110843            110843       Docklands   
 2         2010      1101       110843            110843       Docklands   
 
         trading_name  \
 0             Vacant   
 1         Newsxpress   
 2  Lifestyle Luggage   
 
                                            business_address  \
 0                     163-235 Spencer Street DOCKLANDS 3008   
 1  Shop 302, Ground , 237-261 Spencer Street DOCKLANDS 3008   
 2  Shop 102, Level 1, 163-261 Spencer Street DOCKLANDS 3008   
 
    industry_anzsic4_code        industry_anzsic4_description   longitude  \
 0                      0                        Vacant Space  144.950564   
 1                   4244        Newspaper and Book Retailing  144.950564   
 2                   4259  Other Personal Accessory Retailing  144.950564   
 
     latitude                              point  
 0 -37.814509  -37.8145089728263, 144.9505641424  
 1 -37.814509  -37.8145089728263, 144.9505641424  
 2 -37.814509  -37.8145089728263, 144.9505641424  ,
          business_name   latitude   longitude
 0  Transit Rooftop Bar -37.817778  144.969942
 1         Taxi Kitchen -37.817778  144.969942
 2       Taxi Riverside -37.817778  144.969942,
    location_id      sensor_description sensor_name installation_date  \
 0            3       Melbourne Central    Swa295_T        2009-03-25   
 1            5          Princes Bridge     PriNW_T        2009-03-26   
 2            9  Southern Cross Station    Col700_T        2009-03-23   
 
                               note location_type status direction_1  \
 0                              NaN       Outdoor      A       North   
 1  Replace with: 00:6e:02:01:9e:54       Outdoor      A       North   
 2                              NaN       Outdoor      A        East   
 
   direction_2   latitude   longitude                    location  
 0       South -37.811015  144.964295  -37.81101524, 144.96429485  
 1       South -37.818742  144.967877  -37.81874249, 144.96787656  
 2        West -37.819830  144.951026  -37.81982992, 144.95102555  )

# Merge coordinates 
ped_df = ped_df.merge(
    loc_df[['sensor_name','latitude','longitude','sensor_description']], 
    on='sensor_name', how='left'
)

# Standardise column names and build datetime
ped_df = ped_df.rename(columns={
    'sensing_date': 'date',
    'hourday': 'hour',
    'pedestriancount': 'hourly_count',
    'sensor_description': 'location_name'   
})

ped_df['date'] = pd.to_datetime(ped_df['date'], errors='coerce')
ped_df['hour'] = pd.to_numeric(ped_df['hour'], errors='coerce')
ped_df['date_time'] = pd.to_datetime(
    ped_df['date'].astype(str) + ' ' + ped_df['hour'].astype('Int64').astype(str) + ':00',
    errors='coerce'
)

# Clean coordinate fields
ped_df['latitude']  = pd.to_numeric(ped_df['latitude'], errors='coerce')
ped_df['longitude'] = pd.to_numeric(ped_df['longitude'], errors='coerce')

# Drop invalid rows
ped_df = ped_df.dropna(subset=['latitude','longitude','date_time','hourly_count']).copy()

print("Ped rows after cleaning:", len(ped_df))
ped_df[['location_name','date_time','hourly_count','latitude','longitude']].head(3)

Ped rows after cleaning: 1486330

# Create weekday column
ped_df['weekday'] = ped_df['date_time'].dt.day_name()

# Filter for weekends
weekend_df = ped_df[ped_df['weekday'].isin(['Saturday', 'Sunday'])].copy()

# **Aggregate total weekend count per full location name**
weekend_summary = (
    weekend_df
    .groupby(['location_name', 'latitude', 'longitude'], as_index=False)['hourly_count']
    .sum()
    .rename(columns={'hourly_count': 'total_weekend_count'})
    .sort_values('total_weekend_count', ascending=False)
)

# top 10 busiest weekend locations
top10_weekend = (
    weekend_summary
    .head(10)
    .reset_index(drop=True)  
)


top10_weekend.index = top10_weekend.index + 1
top10_weekend.index.name = "Rank"

# Display final ranked table
top10_weekend

top10_weekend[['location_name','total_weekend_count']].head(10)

def haversine_np(lat1, lon1, lat2, lon2):
    """Vectorized Haversine distance in meters."""
    R = 6371000.0
    phi1, phi2 = np.radians(lat1), np.radians(lat2)
    dphi = np.radians(lat2 - lat1)
    dlambda = np.radians(lon2 - lon1)
    a = np.sin(dphi/2.0)**2 + np.cos(phi1)*np.cos(phi2)*np.sin(dlambda/2.0)**2
    return 2*R*np.arcsin(np.sqrt(a))

nearby_business_counts = []
example_cafes = []

for _, s in top10_weekend.iterrows():
    lat0, lon0 = s['latitude'], s['longitude']

    
    biz_box = business_df[
        (business_df['latitude']  >= lat0-0.002) &
        (business_df['latitude']  <= lat0+0.002) &
        (business_df['longitude'] >= lon0-0.002) &
        (business_df['longitude'] <= lon0+0.002)
    ]
    d_biz = haversine_np(lat0, lon0, biz_box['latitude'].values, biz_box['longitude'].values)
    count_biz = int((d_biz <= 200).sum())
    nearby_business_counts.append(count_biz)

    cafe_box = cafes_df[
        (cafes_df['latitude']  >= lat0-0.002) &
        (cafes_df['latitude']  <= lat0+0.002) &
        (cafes_df['longitude'] >= lon0-0.002) &
        (cafes_df['longitude'] <= lon0+0.002)
    ]
    d_cafe = haversine_np(lat0, lon0, cafe_box['latitude'].values, cafe_box['longitude'].values)
    nearby_cafes = cafe_box[d_cafe <= 200]
    names = ', '.join(nearby_cafes['business_name'].dropna().astype(str).head(3).tolist())
    example_cafes.append(names)


top10_weekend['nearby_businesses'] = nearby_business_counts
top10_weekend['example_cafes'] = example_cafes


top10_weekend[['location_name','total_weekend_count','nearby_businesses','example_cafes']]

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

# Calculate nearby cafés count for each hotspot
cafe_counts = []
for _, sensor in top10_weekend.iterrows():
    subset_cafes = cafes_df[
        (cafes_df['latitude']  >= sensor['latitude']-0.002) &
        (cafes_df['latitude']  <= sensor['latitude']+0.002) &
        (cafes_df['longitude'] >= sensor['longitude']-0.002) &
        (cafes_df['longitude'] <= sensor['longitude']+0.002)
    ]
    distances = haversine_np(sensor['latitude'], sensor['longitude'],
                             subset_cafes['latitude'].values, subset_cafes['longitude'].values)
    cafe_counts.append((distances <= 200).sum())

top10_weekend['nearby_cafes'] = cafe_counts

#  Create 3 subplots (horizontal bars) 
fig, axes = plt.subplots(1, 3, figsize=(22, 10), sharey=True)

#  Foot traffic
axes[0].barh(top10_weekend['location_name'], top10_weekend['total_weekend_count'], color='steelblue')
axes[0].set_title("Top 10 Weekend Pedestrian Hotspots", fontsize=14)
axes[0].set_xlabel("Total Weekend Foot Traffic")
axes[0].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
axes[0].invert_yaxis()
for i, v in enumerate(top10_weekend['total_weekend_count']):
    axes[0].text(v + 50000, i, f"{v:,}", va='center', fontsize=8, color='black')

#  Nearby businesses
axes[1].barh(top10_weekend['location_name'], top10_weekend['nearby_businesses'], color='orange')
axes[1].set_title("Nearby Businesses within 200m", fontsize=14)
axes[1].set_xlabel("Number of Nearby Businesses")
axes[1].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
axes[1].invert_yaxis()
for i, v in enumerate(top10_weekend['nearby_businesses']):
    axes[1].text(v + 50, i, f"{v:,}", va='center', fontsize=8, color='black')

#  Nearby cafés
axes[2].barh(top10_weekend['location_name'], top10_weekend['nearby_cafes'], color='green')
axes[2].set_title("Nearby Cafés/Restaurants within 200m", fontsize=14)
axes[2].set_xlabel("Number of Nearby Cafés")
axes[2].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
axes[2].invert_yaxis()
for i, v in enumerate(top10_weekend['nearby_cafes']):
    axes[2].text(v + 5, i, f"{v:,}", va='center', fontsize=8, color='black')


plt.subplots_adjust(wspace=0.4)
plt.tight_layout()
plt.show()

# Add day_type column 
ped_df['day_of_week'] = ped_df['date_time'].dt.day_name()
ped_df['day_type'] = ped_df['day_of_week'].apply(
    lambda x: "Weekend" if x in ["Saturday","Sunday"] else "Weekday"
)

# Group by descriptive location and day_type 
compare_avg = ped_df.groupby(["location_name","day_type"])["hourly_count"].mean().unstack()

#  Top 5 busiest locations 
compare_top5_avg = compare_avg.loc[
    compare_avg.sum(axis=1).sort_values(ascending=False).head(5).index
]


compare_top5_avg.plot(
    kind="bar",
    figsize=(10,6),
    color=["#1f77b4", "#ff7f0e"]  
)

plt.title("Average Daily Pedestrian Counts: Weekend vs Weekday (Top 5 Locations)", fontsize=14)
plt.ylabel("Average Pedestrian Count per Day")
plt.xlabel("Location")
plt.xticks(rotation=45, ha="right")
plt.legend(title="Day Type")
plt.tight_layout()
plt.show()

#  Print percentage differences with names 
percent_diff = (
    (compare_top5_avg["Weekend"] - compare_top5_avg["Weekday"])
    / compare_top5_avg["Weekday"]
) * 100

print("\n--- Weekend vs Weekday % Difference (Top 5 Locations) ---\n")
for loc, diff in percent_diff.items():
    if diff > 0:
        print(f"{loc}: +{diff:.1f}% higher on weekends")
    else:
        print(f"{loc}: {diff:.1f}% lower on weekends")

--- Weekend vs Weekday % Difference (Top 5 Locations) ---

Flinders La-Swanston St (West): +6.9% higher on weekends
Southbank: +14.2% higher on weekends
Elizabeth St - Flinders St (East) - New footpath: -5.8% lower on weekends
368 Elizabeth Street: +2.3% higher on weekends
State Library - New: +9.1% higher on weekends

#  Get busiest location details
busiest_lat = top10_weekend.iloc[0]['latitude']
busiest_lon = top10_weekend.iloc[0]['longitude']
busiest_name = top10_weekend.iloc[0]['location_name']   

#  Filter all businesses within 200m of this location
distances_all = haversine_np(
    busiest_lat, busiest_lon,
    business_df['latitude'].values,
    business_df['longitude'].values
)
nearby_all_businesses = business_df[distances_all <= 200]

#  Load cafés dataset if not already loaded
CAFES_DATASET = 'cafes-and-restaurants-with-seating-capacity'
cafes_df = fetch_dataset_csv(BASE_URL, CAFES_DATASET, API_KEY)

# Convert coordinates to numeric for cafés dataset
cafes_df['latitude'] = pd.to_numeric(cafes_df['latitude'], errors='coerce')
cafes_df['longitude'] = pd.to_numeric(cafes_df['longitude'], errors='coerce')
cafes_df = cafes_df.dropna(subset=['latitude', 'longitude'])

#  Filter cafes/restaurants within 200m
distances_cafes = haversine_np(
    busiest_lat, busiest_lon,
    cafes_df['latitude'].values,
    cafes_df['longitude'].values
)
nearby_cafes = cafes_df[distances_cafes <= 200]

#  Prepare counts for pie chart
cafe_count = len(nearby_cafes)
other_count = len(nearby_all_businesses) - cafe_count

#  Plot pie chart
plt.figure(figsize=(6, 6))
plt.pie(
    [cafe_count, other_count],
    labels=['Cafes/Restaurants', 'Other Businesses'],
    autopct='%1.1f%%',
    startangle=140,
    colors=['#ff9999','#66b3ff']
)
plt.title(f"Business Composition within 200m of {busiest_name}")
plt.show()

print(f"{busiest_name} has {cafe_count} cafes/restaurants out of {len(nearby_all_businesses)} total businesses nearby.")

Southbank has 1157 cafes/restaurants out of 4336 total businesses nearby.

if 'trading_name' in cafes_df.columns:
    top5_cafes = nearby_cafes[['trading_name', 'seating_type', 'number_of_seats', 'latitude', 'longitude']].head(5)
elif 'business_name' in cafes_df.columns:
    top5_cafes = nearby_cafes[['business_name', 'latitude', 'longitude']].head(5)
else:
    top5_cafes = nearby_cafes.head(5)

print(f"\nTop 5 Cafes/Restaurants within 200m of {busiest_name}:\n")  

for idx, row in top5_cafes.iterrows():
    name = row.get('trading_name', row.get('business_name', 'Unknown'))
    seats = row.get('number_of_seats', 'N/A')
    seat_info = f" – {seats} seats" if seats != 'N/A' else ""
    print(f"- {name}{seat_info}")

Top 5 Cafes/Restaurants within 200m of Southbank:

- Miyako Japanese Cuisine & Teppanyaki – 40 seats
- Bluetrain – 180 seats
- The Deck – 20 seats
- Pure South Dining – 20 seats
- Grill'd – 30 seats

from branca.element import Template, MacroElement
import folium
import os

#  Ensure nearby_businesses column exists 
if 'nearby_businesses' not in top10_weekend.columns:
    nearby_business_counts = []
    for _, s in top10_weekend.iterrows():
        lat0, lon0 = s['latitude'], s['longitude']

        biz_box = business_df[
            (business_df['latitude']  >= lat0-0.002) &
            (business_df['latitude']  <= lat0+0.002) &
            (business_df['longitude'] >= lon0-0.002) &
            (business_df['longitude'] <= lon0+0.002)
        ]
        d_biz = haversine_np(lat0, lon0, biz_box['latitude'].values, biz_box['longitude'].values)
        count_biz = int((d_biz <= 200).sum())
        nearby_business_counts.append(count_biz)

    top10_weekend['nearby_businesses'] = nearby_business_counts

#  Ensure example_cafes column exists 
if 'example_cafes' not in top10_weekend.columns:
    example_cafes = []
    for _, s in top10_weekend.iterrows():
        lat0, lon0 = s['latitude'], s['longitude']
        cafe_box = cafes_df[
            (cafes_df['latitude']  >= lat0-0.002) &
            (cafes_df['latitude']  <= lat0+0.002) &
            (cafes_df['longitude'] >= lon0-0.002) &
            (cafes_df['longitude'] <= lon0+0.002)
        ]
        d_cafe = haversine_np(lat0, lon0, cafe_box['latitude'].values, cafe_box['longitude'].values)
        nearby_cafes = cafe_box[d_cafe <= 200]
        names = ', '.join(nearby_cafes['business_name'].dropna().astype(str).head(3).tolist())
        example_cafes.append(names)
    top10_weekend['example_cafes'] = example_cafes

#  Create map 
melbourne_map2 = folium.Map(location=[-37.8136, 144.9631], zoom_start=13)

for _, row in top10_weekend.iterrows():
    if row['nearby_cafes'] >= 50:
        marker_color = 'darkgreen'
    elif row['nearby_cafes'] >= 20:
        marker_color = 'orange'
    else:
        marker_color = 'lightblue'

    if row['example_cafes'] and row['example_cafes'] != "—":
        li_items = "".join(f"<li>{n}</li>" for n in row['example_cafes'].split(", "))
        cafes_list_html = f"<b>Nearby cafés (≤200m):</b><ul style='margin:4px 0 0 18px;'>{li_items}</ul>"
    else:
        cafes_list_html = "<b>Nearby cafés (≤200m):</b> —"

    popup_text = (
        f"<b>{row['location_name']}</b><br>"
        f"Weekend Foot Traffic: {int(row['total_weekend_count']):,}<br>"
        f"Nearby Businesses (≤200m): {int(row['nearby_businesses']):,}<br>"
        f"Nearby Cafés (≤200m): {int(row['nearby_cafes']):,}<br>"
        f"{cafes_list_html}"
    )

    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=popup_text,
        icon=folium.Icon(color=marker_color, icon='briefcase')
    ).add_to(melbourne_map2)


legend_html = """
{% macro html(this, kwargs) %}
<div style="
    position: fixed; bottom: 50px; left: 50px;
    width: 220px; z-index:9999; font-size:14px;
    background-color: white; border:2px solid grey; border-radius:8px; padding: 10px;">
<b>Café Density Legend</b><br>
<i class="fa fa-map-marker fa-2x" style="color:darkgreen"></i> 50+ cafés<br>
<i class="fa fa-map-marker fa-2x" style="color:orange"></i> 20–49 cafés<br>
<i class="fa fa-map-marker fa-2x" style="color:lightblue"></i> <20 cafés
</div>
{% endmacro %}
"""
legend = MacroElement()
legend._template = Template(legend_html)
melbourne_map2.get_root().add_child(legend)

# Show interactive map locally 
melbourne_map2

#  Save interactive HTML 
map_html_file = "Top10_Weekend_Map.html"
melbourne_map2.save(map_html_file)
print(f" Interactive map saved as {map_html_file}")


png_file = "Top10_Weekend_Map.png"
if os.path.exists(png_file):
    from IPython.display import Image, display
    display(Image(png_file))
    print("🖼️ Static PNG preview displayed (for GitHub).")
else:
    print("⚠️ No static PNG found. If needed, open the HTML map in a browser, take a screenshot, save as Top10_Weekend_Map.png, and commit it.")

✅ Interactive map saved as Top10_Weekend_Map.html

🖼️ Static PNG preview displayed (for GitHub).

def fmt_int(x):
    try:
        return f"{int(x):,}"
    except:
        return str(x)

print("\n--- Weekend Business Activity Insights ---\n")

# Busiest location
busiest = top10_weekend.iloc[0]
print(f"- Busiest weekend location: {busiest['location_name']} "
      f"with {fmt_int(busiest['total_weekend_count'])} people.")

# Top 3 locations
print("\n- Top 3 weekend hotspots (with nearby businesses & café examples):")
for i in range(min(3, len(top10_weekend))):
    r = top10_weekend.iloc[i]
    cafes = r.get('example_cafes', '—') or '—'
    print(f"  {i+1}. {r['location_name']} – {fmt_int(r['total_weekend_count'])} people; "
          f"{fmt_int(r.get('nearby_businesses', 0))} businesses; Cafés: {cafes}")

# Recommendations
print("\n- Recommendations:")
print("  • Extend weekend opening hours in identified hotspots.")
print("  • Run targeted weekend promotions where both footfall and business presence are high.")
print("  • Consider pop-ups/events within ≤200 m of top hotspots to capture leisure traffic.")

--- Weekend Business Activity Insights ---

- Busiest weekend location: Southbank with 7,817,620 people.

- Top 3 weekend hotspots (with nearby businesses & café examples):
  1. Southbank – 7,817,620 people; 4,292 businesses; Cafés: Miyako Japanese Cuisine & Teppanyaki, Bluetrain, The Deck
  2. Flinders La-Swanston St (West) – 6,770,532 people; 20,034 businesses; Cafés: RMB Cafe, The Mill House Bar, Il Tempo 2 Pasta
  3. State Library - New – 6,296,885 people; 12,805 businesses; Cafés: Gong Cha, Harajuku Crepes, Theobroma Chocolate Lounge

- Recommendations:
  • Extend weekend opening hours in identified hotspots.
  • Run targeted weekend promotions where both footfall and business presence are high.
  • Consider pop-ups/events within ≤200 m of top hotspots to capture leisure traffic.

out = top10_weekend[['location_name','latitude','longitude',
                     'total_weekend_count','nearby_businesses','example_cafes']].copy()

out.to_csv("Top10_Weekend_BusinessImpact.csv", index=False)
print("Saved:", "Top10_Weekend_BusinessImpact.csv")

Saved: Top10_Weekend_BusinessImpact.csv

import os
print(os.path.abspath("Top10_Weekend_Map.html"))

/Users/pinnamsahithi/Desktop/Top10_Weekend_Map.html

	location_name	date_time	hourly_count	latitude	longitude
0	Flinders Ln -Degraves St (North)	2024-10-27 15:00:00	445	-37.816848	144.965598
1	231 Bourke St	2024-11-13 20:00:00	388	-37.813331	144.966756
2	231 Bourke St	2025-03-12 13:00:00	845	-37.813331	144.966756

	location_name	latitude	longitude	total_weekend_count
Rank
1	Southbank	-37.820187	144.965085	7817620
2	Flinders La-Swanston St (West)	-37.816686	144.966897	6770532
3	State Library - New	-37.810578	144.964443	6296885
4	Elizabeth St - Flinders St (East) - New footpath	-37.817980	144.965034	5876190
5	Melbourne Central-Elizabeth St (East)	-37.812585	144.962578	4722217
6	Town Hall (West)	-37.814880	144.966088	4553044
7	Little Collins St-Swanston St (East)	-37.814141	144.966094	4085416
8	Melbourne Central	-37.811015	144.964295	4074855
9	Melbourne Convention Exhibition Centre	-37.824018	144.956044	4038193
10	The Arts Centre	-37.821299	144.968793	3899753

	location_name	total_weekend_count
Rank
1	Southbank	7817620
2	Flinders La-Swanston St (West)	6770532
3	State Library - New	6296885
4	Elizabeth St - Flinders St (East) - New footpath	5876190
5	Melbourne Central-Elizabeth St (East)	4722217
6	Town Hall (West)	4553044
7	Little Collins St-Swanston St (East)	4085416
8	Melbourne Central	4074855
9	Melbourne Convention Exhibition Centre	4038193
10	The Arts Centre	3899753

	location_name	total_weekend_count	nearby_businesses	example_cafes
Rank
1	Southbank	7817620	4292	Miyako Japanese Cuisine & Teppanyaki, Bluetrain, The Deck
2	Flinders La-Swanston St (West)	6770532	20034	RMB Cafe, The Mill House Bar, Il Tempo 2 Pasta
3	State Library - New	6296885	12805	Gong Cha, Harajuku Crepes, Theobroma Chocolate Lounge
4	Elizabeth St - Flinders St (East) - New footpath	5876190	12989	Subway, Mad Mex Flinders Lane, RMB Cafe
5	Melbourne Central-Elizabeth St (East)	4722217	17257	La Belle Miette, Le Petite Creperie, Big Boy BBQ
6	Town Hall (West)	4553044	23571	Health Cosmos, Box On Collins Restaurant, La Vita Buona 'The Good Life' Cellar Bar Deli
7	Little Collins St-Swanston St (East)	4085416	18753	Oh Deer, Salero Kito Padang Melbourne, Sambal Kampung
8	Melbourne Central	4074855	15982	1000 Wat, 8 Bit, Gong Cha
9	Melbourne Convention Exhibition Centre	4038193	170	Crown Plaza, Crown Plaza, Crown Plaza
10	The Arts Centre	3899753	516	Mezz Bar, Jarrah Restaurant & Bar, Sake Restaurant & Bar

📍 Weekend Business Activity Map¶